Mathias STRASSER
b7dc27f2a5
Les enseignants ont besoin de moyennes à jour immédiatement après la publication ou modification des notes, sans attendre un batch nocturne. Le système recalcule via Domain Events synchrones : statistiques d'évaluation (min/max/moyenne/médiane), moyennes matières pondérées (normalisation /20), et moyenne générale par élève. Les résultats sont stockés dans des tables dénormalisées avec cache Redis (TTL 5 min). Trois endpoints API exposent les données avec contrôle d'accès par rôle. Une commande console permet le backfill des données historiques au déploiement.
58 lines
2.5 KiB
Markdown
58 lines
2.5 KiB
Markdown
**Language:** Use `{communication_language}` for all output.
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**Output Language:** Use `{document_output_language}` for documents.
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**Output Location:** `{planning_artifacts}`
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# Stage 2: Contextual Discovery
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**Goal:** Armed with the user's stated intent, intelligently gather and synthesize all available context — documents, project knowledge, and web research — so later stages work from a rich, relevant foundation.
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## Subagent Fan-Out
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Now that you know what the brief is about, fan out subagents in parallel to gather context. Each subagent receives the product intent summary so it knows what's relevant.
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**Launch in parallel:**
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1. **Artifact Analyzer** (`../agents/artifact-analyzer.md`) — Scans `{planning_artifacts}` and `{project_knowledge}` for relevant documents. Also scans any specific paths the user provided. Returns structured synthesis of what it found.
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2. **Web Researcher** (`../agents/web-researcher.md`) — Searches for competitive landscape, market context, trends, and relevant industry data. Returns structured findings scoped to the product domain.
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### Graceful Degradation
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If subagents are unavailable or fail:
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- Read only the most relevant 1-2 documents in the main context and summarize (don't full-read everything — limit context impact in degraded mode)
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- Do a few targeted web searches inline
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- Never block the workflow because a subagent feature is unavailable
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## Synthesis
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Once subagent results return (or inline scanning completes):
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1. **Merge findings** with what the user already told you
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2. **Identify gaps** — what do you still need to know to write a solid brief?
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3. **Note surprises** — anything from research that contradicts or enriches the user's assumptions?
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## Mode-Specific Behavior
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**Guided mode:**
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- Present a concise summary of what you found: "Here's what I learned from your documents and web research..."
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- Highlight anything surprising or worth discussing
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- Share the gaps you've identified
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- Ask: "Anything else you'd like to add, or shall we move on to filling in the details?"
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- Route to `guided-elicitation.md`
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**Yolo mode:**
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- Absorb all findings silently
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- Skip directly to `draft-and-review.md` — you have enough to draft
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- The user will refine later
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**Headless mode:**
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- Absorb all findings
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- Skip directly to `draft-and-review.md`
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- No interaction
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## Stage Complete
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This stage is complete when subagent results (or inline scanning fallback) have returned and findings are merged with user context. Route per mode:
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- **Guided** → `guided-elicitation.md`
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- **Yolo / Headless** → `draft-and-review.md`
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